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In the nicely written article https://arxiv.org/abs/1803.06917 by Justin Sirignano and Rama Cont, they explained that their model is universal and stationary. I am a bit confused about some questions.

  1. What makes that model any different from other models?
  2. What do they called the "Universal Features"?
  3. As they use petabyte of data, i.e. 1000 stocks over on 3 years, can we achieve the same results with 3 stocks over 3 years instead?
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Its called 'universal' because, unlike usual models trained on time series for a given stock/ contract, this model is trained on a POOLED data set (in this case 500 or so stocks) and is then shown to be applicable for forecasting any stock, including those not included in the training data. This is different from the usual approach where, say, you use time series of IBM prices to estimate/train a model for IBM, then data for GOOGL to train/estimate a modle for GOOGL etc. Here they pool all data, train then use the model to forecast any stock. At first glance it seems nonsense but amazingly it works better than without pooling.

I think thats the main point of the paper.

This 'universality' and the superiority of training on pooled data has been confirmed by other papers which have replicated their results (for ex. Zohren et al)

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